Investigating the use of an Artificial Intelligence Model in an ERP Cloud-Based System

Yathiraju, Nikhitha

doi:10.22161/eec.72.1

Cited by 80 publications

(19 citation statements)

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“…Banks and FinTech firms sometimes fight for the same market share as technology becomes increasingly important in the financial sector [20,21].Extraordinary discoveries, trends, and technologies that are shaping the future of banking have emerged owing to this ecosystem of co-development and healthy competition. All these benefits are possible with Big Data and Metaverse [22,23,24].…”

Section: Implications Of the Studymentioning

confidence: 99%

Untitled

2023

IJCSIT

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The Fintech industry is no stranger to transformation and disruption. With the emergence of new technologies such as Big Data and the Metaverse, finance companies are quickly leveraging these technologies to revolutionize the virtual fintech sector. Big data transforms how financial institutions gather, analyze, and utilize data. The sheer amount of data available has opened up countless opportunities for better customer understanding and insight into business operations. Companies can use this data to create predictive models that identify emerging trends in customer behavior, market activity, and other aspects of their business. By utilizing these insights, companies can make smarter decisions about products, services, marketing strategies, and more. The Metaverse is another technology with massive potential for disrupting the Fintech sector. The Metaverse enables businesses to offer virtual goods and services without physical stores or retail outlets. This makes it easier for companies to scale their operations across multiple regions and gives customers more options when choosing a financial service provider. Additionally, using digital currencies like Bitcoin helps reduce transaction costs significantly while increasing security compared to traditional payment methods like credit cards or PayPal.

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Section: Implications Of the Studymentioning

confidence: 99%

Untitled

2023

IJCSIT

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“…We are aware of and grateful for the research on business information systems, specifically ERP that has been done. These studies have proposed conceptual frameworks for thinking about customization from the perspective of benefits realization and methods for both qualitatively and quantitatively estimating the benefits of customization (Luo and Strong, 2004; Ashurst et al , 2008; Parthasarathy, 2008; Rothenberger and Srite, 2009; Staehr, 2010; Daneva and Wieringa, 2010; Aslam et al , 2012; Eckarz et al , 2012; Norton et al , 2013; Doherty, 2014; Parthasarathy and Daneva, 2014; Parthasarathy and Sharma, 2014; Parthasarathy and Daneva, 2016; Parthasarathy and Sharma, 2016; Parthasarathy and Sharma, 2017; Ibrahim et al , 2019; Parthasarathy et al , 2020; Mahmood et al , 2020; Febrianto and Soediantono, 2022; Yathiraju, 2022; Yoo and Kim, 2021; Wang et al , 2022).…”

Section: Enterprise Resource Planning Software Customizationmentioning

confidence: 99%

“…Due to the evolution of AI and ML, companies are reinventing their old ERP software (Yathiraju, 2022). In this era of digital competition, customized systems are failing to deliver the desired outcomes.…”

Section: Artificial Intelligence In Enterprise Resource Planning Soft...mentioning

confidence: 99%

“…For an implementation to be effective, it is critical to minimize the customization and maintain the current business processes. In practice, ERP software customization is carried out with the help of conceptual frameworks suggested by previous studies (Brehm et al , 2001; Luo and Strong, 2004; Rothenberger and Srite, 2009; Zach et al , 2014; Mahmood et al , 2020; Febrianto and Soediantono, 2022; Yathiraju, 2022; Wang et al , 2022). These frameworks are used by ERP vendors to develop an automated software tool grounded on an algorithm.…”

Section: Introductionmentioning

confidence: 99%

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Understanding algorithm bias in artificial intelligence-enabled ERP software customization

Parthasarathy¹,

Padmapriya²

2023

JEET

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Purpose Algorithm bias refers to repetitive computer program errors that give some users more weight than others. The aim of this article is to provide a deeper insight of algorithm bias in AI-enabled ERP software customization. Although algorithmic bias in machine learning models has uneven, unfair and unjust impacts, research on it is mostly anecdotal and scattered. Design/methodology/approach As guided by the previous research (Akter et al., 2022), this study presents the possible design bias (model, data and method) one may experience with enterprise resource planning (ERP) software customization algorithm. This study then presents the artificial intelligence (AI) version of ERP customization algorithm using k-nearest neighbours algorithm. Findings This study illustrates the possible bias when the prioritized requirements customization estimation (PRCE) algorithm available in the ERP literature is executed without any AI. Then, the authors present their newly developed AI version of the PRCE algorithm that uses ML techniques. The authors then discuss its adjoining algorithmic bias with an illustration. Further, the authors also draw a roadmap for managing algorithmic bias during ERP customization in practice. Originality/value To the best of the authors’ knowledge, no prior research has attempted to understand the algorithmic bias that occurs during the execution of the ERP customization algorithm (with or without AI).

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“…Within the industry, media providers, such as the Associated Press, are using AI-enabled technologies in the production and distribution of news (Marconi et al , 2017). There is still limited knowledge regarding the integration of this technology in management practices, especially in cloud ERP systems (Yathiraju, 2022).…”

Section: Introductionmentioning

confidence: 99%

Explaining the effect of artificial intelligence on the technology acceptance model in media: a cloud computing approach

Tavakoli

Mozaffari

Danaei

et al. 2022

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Purpose The purpose of this paper is to explain the effect of the technology acceptance model in the media environment by using the mediating role of artificial intelligence and the cloud computing approach. Design/methodology/approach After reviewing the theoretical foundations, a conceptual model framework and research hypotheses were formed. The statistical population of the study included managers, deputies and experts from the National Iranian Oil Company, and a statistical sample of 368 people was selected by simple random sampling. Findings The results of structural equation modelling with PLS 2.0 software show a positive and significant effect on the artificial intelligence variable in the technology acceptance model with the cloud approach. Artificial intelligence has opened a new space in the digital world, especially in the media, so that its profound impact is quite evident and has affected people’s lives. Originality/value The acceleration of various technologies has severely challenged the approach of organizations, especially the media. The media environment with word of the technologies of the Industry 4.0, especially cloud computing technology, has changed the ways of accessing and using products and services.

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Investigating the use of an Artificial Intelligence Model in an ERP Cloud-Based System

Cited by 80 publications

References 0 publications

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Understanding algorithm bias in artificial intelligence-enabled ERP software customization

Explaining the effect of artificial intelligence on the technology acceptance model in media: a cloud computing approach

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